Automated detection of objects in a biological sample

ABSTRACT

A method, system, and apparatus are provided for automated light microscopic for detection of proteins associated with cell proliferative disorders.

CLAIM OF PRIORITY

This application claims the benefit of priority, under 35 USC§119(e)(1), of U.S. provisional application No. 60/143,823, filed Jul.13, 1999, and is a continuation of U.S. patent application Ser. No.08/758,436, filed Nov. 27, 1996, which claims the benefit of U.S.Provisional Application No. 60/026,805, filed on Nov. 30, 1995.

TECHNICAL FIELD

The invention relates generally to light microscopy and, moreparticularly, to automated light microscopic methods and an apparatusfor detection of proteins associated with cell proliferative disorders.

BACKGROUND OF THE INVENTION

In the field of medical diagnostics including oncology, the detection,identification, quantitation and characterization of cells of interest,such as cancer cells, through testing of biological specimens is animportant aspect of diagnosis. Typically, a biological specimen such asbone marrow, lymph nodes, peripheral blood, cerebrospinal fluid, urine,effusions, fine needle aspirates, peripheral blood scrapings or othermaterials are prepared by staining the specimen to identify cells ofinterest. One method of cell specimen preparation is to react a specimenwith a specific probe which can be a monoclonal antibody, a polyclonalantiserum, or a nucleic acid which is reactive with a component of thecells of interest, such as tumor cells. The reaction may be detectedusing an enzymatic reaction, such as alkaline phosphatase or glucoseoxidase or peroxidase to convert a soluble colorless substrate to acolored insoluble precipitate, or by directly conjugating a dye to theprobe. Examination of biological specimens in the past has beenperformed manually by either a lab technician or a pathologist. In themanual method, a slide prepared with a biological specimen is viewed ata low magnification under a microscope to visually locate candidatecells of interest. Those areas of the slide where cells of interest arelocated are then viewed at a higher magnification to confirm thoseobjects as cells of interest, such as tumor or cancer cells. The manualmethod is time consuming and prone to error including missing areas ofthe slide. Automated cell analysis systems have been developed toimprove the speed and accuracy of the testing process. One knowninteractive system includes a single high power microscope objective forscanning a rack of slides, portions of which have been previouslyidentified for assay by an operator. In that system, the operator firstscans each slide at a low magnification similar to the manual method andnotes the points of interest on the slide for later analysis. Theoperator then stores the address of the noted location and theassociated function in a data file. Once the points of interest havebeen located and stored by the operator, the slide is then positioned inan automated analysis apparatus which acquires images of the slide atthe marked points and performs an image analysis.

A number of cellular proteins are related to cell proliferation and cellsignaling. Many of these proteins are critical for normal cell growth.For example, HER2 (neu) is a growth factor receptor and when foundwithin tumor cells amounts to an aggressively growing tumor. Studieshave determined that a significantly decreased disease-free survival andoverall survival of a patient with over-expression of HER2. Before anoncologist prescribes an anti-HER2/neu therapeutic, animmunohistochemistry (IHC) assessment for HER2/neu is desirable.Therapeutic availability increases the need for a standard methodologyfor assessing the expression of HER2/neu.

Three general methods are currently available for the detection ofHER2/neu: genetic detection, protein expression, and protein activity.In situ hybridization methods are typically used for HER2/neu geneticdetection. Immunohistochemistry methods are used for the assessment ofHER2/neu protein expression.

SUMMARY OF THE INVENTION

A problem with the foregoing automated system is the continued need foroperator input to initially locate cell objects for analysis. Suchcontinued dependence on manual input can lead to errors including cellsof interest being missed. Such errors can be critical especially inassays for so-called rare events, e.g., finding one tumor cell in a cellpopulation of one million normal cells. Additionally, manual methods canbe extremely time consuming and can require a high decree of training toidentify and/or quantify cells. This is not only true for tumor celldetection, but also for other applications ranging from neutrophilalkaline phosphatase assays, reticulocyte counting and maturationassessment, and others. The associated manual labor leads to a high costfor these procedures in addition to the potential errors that can arisefrom long, tedious manual examinations. A need exists, therefore, for animproved automated cell analysis system which can quickly and accuratelyscan large amounts of biological material on a slide. Accordingly, thepresent invention provides a method and apparatus for automated cellanalysis which eliminates the need for operator input to locate cellobjects for analysis.

In accordance with the present invention, a slide prepared with abiological specimen and reagent is placed in a slide carrier whichpreferably holds four slides. The slide carriers are loaded into aninput hopper of the automated system. The operator may then enter dataidentifying the size, shape, and location of a scan area on each slide,or, preferably, the system automatically locates a scan area for eachslide during slide processing. The operator then activates the systemfor slide processing. At system activation, a slide carrier ispositioned on an X-Y stage of an optical system. Any bar codes used toidentify slides are then read and stored for each slide in a carrier.The entire slide is rapidly scanned at a low magnification, typically10×. At each location of the scan, a low magnification image is acquiredand processed to detect candidate objects of interest. Preferably,color, size, and shape are used to identify objects of interest. Thelocation of each candidate object of interest is stored.

At the completion of the low level scan for each slide in the carrier onthe stage, the optical system is adjusted to a high magnification suchas 40× or 60×, and the X-Y stage is positioned to the stored locationsfor the candidate objects of interest on each slide in the carrier. Ahigh magnification image is acquired for each candidate object ofinterest and a series of image processing steps are performed to confirmthe analysis which was performed at low magnification. A highmagnification image is stored for each confirmed object of interest.

Additionally, control slides including positive and negative controls,may be used to determine background staining. For example, positivecontrol slides for a particular staining technique can be run followedby a negative control in order to determine a delta for the controls.Subsequent scanning for objects of interest may then differentiate suchobjects based upon their color or intensity above the delta.

These images are then available for retrieval by a pathologist orcytotechnologist to review for final diagnostic evaluation. Havingstored the location of each object of interest, a mosaic comprised ofthe candidate objects of interest for a slide may be generated andstored. The pathologist or cytotechnologist may view the mosaic or mayalso directly view the slide at the location of an object of interest inthe mosaic for further evaluation. The mosaic may be stored on magneticmedia for future reference or may be transmitted to a remote site forreview and/or storage. The entire process involved in examining a singleslide takes on the order of 2-15 minutes depending on scan area size andthe number of detected candidate objects of interest.

The present invention has utility in the field of oncology for the earlydetection of minimal residual disease (“micrometastases”). Other usefulapplications include prenatal diagnosis of fetal cells in maternal bloodand in the field of infectious diseases to identify pathogens and viralloads, alkaline phosphatase assessments, reticulocyte counting, andothers. The processing of images acquired in the automated scanning ofthe present invention preferably includes the steps of transforming theimage to a different color space; filtering the transformed image with alow pass filter; dynamically thresholding the pixels of the filteredimage to suppress background material; performing a morphologicalfunction to remove artifacts from the thresholded image; analyzing thethresholded image to determine the presence of one or more regions ofconnected pixels having the same color; and categorizing every regionhaving a size greater than a minimum size as a candidate object ofinterest.

According to another aspect of the invention, the scan area isautomatically determined by scanning the slide; acquiring an image ateach slide position; analyzing texture information of each image todetect the edges of the specimen; and storing the locationscorresponding to the detected edges to define the scan area. Accordingto yet another aspect of the invention, automated focusing of theoptical system is achieved by initially determining a focal plane froman array of points or locations in the scan area. The derived focalplane enables subsequent rapid automatic focusing in the low powerscanning operation. The focal plane is determined by determining properfocal positions across an array of locations and performing an analysissuch as a least squares fit of the array of focal positions to yield afocal plane across the array. Preferably, a focal position at eachlocation is determined by incrementing the position of a Z stage for afixed number of coarse and fine iterations. At each iteration, an imageis acquired and a pixel variance or other optical parameter about apixel mean for the acquired image is calculated to form a set ofvariance data. A least squares fit is performed on the variance dataaccording to a known function. The peak value of the least squares fitcurve is selected as an estimate of the best focal position.

In another aspect of the present invention, another focal positionmethod for high magnification locates a region of interest centeredabout a candidate object of interest within a slide which were locatedduring an analysis of the low magnification images. The region ofinterest is preferably n columns wide, where n is a power of 2. Thepixels of this region are then processed using a Fast Fourier Transformto generate a spectra of component frequencies and corresponding complexmagnitude for each frequency component. Magnitudes of the frequencycomponents which range from 25% to 75% of the maximum frequencycomponent are squared and summed to obtain the total power for theregion of interest. This process is repeated for other Z positions andthe Z position corresponding to the maximum total power for the regionof interest is selected as the best focal position.

According to still another aspect of the invention, a method andapparatus for automated slide handling is provided. A slide is mountedonto a slide carrier with a number of other slides side-by-side. Theslide carrier is positioned in an input feeder with other slide carriersto facilitate automatic analysis of a batch of slides. The slide carrieris loaded onto the X-Y stage of the optical system for the analysis ofthe slides thereon. Subsequently, the first slide carrier is unloadedinto an output feeder after automatic image analysis and the nextcarrier is automatically loaded.

In a specific embodiment the invention provides an automated system forthe quantitation of proteins associated with cell proliferativedisorders, such as HER2/neu expression in tissue. The invention isuseful to determine the over-expression of HER2 in tissue, especiallybreast tissue.

DESCRIPTION OF THE DRAWINGS

The above and other features of the invention including various noveldetails of construction and combinations of parts will now be moreparticularly described with reference to the accompanying drawings andpointed out in the claims. It will be understood that the particularapparatus embodying the invention is shown by way of illustration onlyand not as a limitation of the invention. The principles and features ofthis invention may be employed in varied and numerous embodimentswithout departing from the scope of the invention.

FIG. 1 is a perspective view of an apparatus for automated cell analysisembodying the present invention.

FIG. 2 is a block diagram of the apparatus shown in FIG. 1.

FIG. 3 is a block diagram of the microscope controller of FIG. 2.

FIG. 4 is a plan view of the apparatus of FIG. 1 having the housingremoved.

FIG. 5 is a side view of a microscope subsystem of the apparatus of FIG.1.

FIG. 6a is a top view of a slide carrier for use with the apparatus ofFIG. 1.

FIG. 6b is a bottom view of the slide carrier of FIG. 6a.

FIG. 7a is a top view of an automated slide handling subsystem of theapparatus of FIG. 1.

FIG. 7b is a partial cross-sectional view of the automated slidehandling subsystem of FIG. 7a taken on line A—A.

FIG. 8 is an end view of the input module of the automated slidehandling subsystem. 8 a-8 d illustrate the input operation of theautomatic slide handling subsystem.

FIGS. 9a-9 d illustrate the output operation of the automated slidehandling subsystem.

FIG. 10 is a flow diagram of the procedure for automatically determininga scan area.

FIG. 11 shows the scan path on a prepared slide in the procedure of FIG.10.

FIG. 12 illustrates an image of a field acquired in the procedure ofFIG. 10.

FIG. 13A is a flow diagram of a preferred procedure of redeterminingfocal position.

FIG. 13B is a flow diagram of a preferred procedure for determining afocal position for neutrophils stained with Fast Red and counterstainedwith hemotoxylin.

FIG. 14 is a flow diagram of a procedure for automatically determininginitial focus.

FIG. 15 shows an array of slide positions for use in the procedure ofFIG. 14.

FIG. 16 is a flow diagram of a procedure for automatic focusing at ahigh magnification.

FIG. 17A is a flow diagram of an overview of the preferred process tolocate and identify objects of interest in a stained biological specimenon a slide.

FIG. 17B is a flow diagram of a procedure for color space conversion.

FIG. 18 is a flow diagram of a procedure for background suppression viadynamic thresholding.

FIG. 19 is a flow diagram of a procedure for morphological processing.

FIG. 20 is a flow diagram of a procedure for blob analysis.

FIG. 21 is a flow diagram of a procedure for image processing at a highmagnification.

FIG. 22 illustrates a mosaic of cell images produced by the apparatus.

FIG. 23 is a flow diagram of a procedure for estimating the number ofnucleated cells in an objective field.

FIG. 24 illustrates the apparatus functions available in a userinterface of the apparatus.

DETAILED DESCRIPTION Overview

The invention provides an automated tissue image analysis method for thedetection of a cell proliferative disorder, for example those associatedwith HER2/neu. Other automated tissue image analysis methods, such asimmunohistochemical analyses of estrogen receptor and progesteronereceptor are also provided. Other methods are within the scope of theinvention, such as analyses for MM/MRD for tissue, MIB-I, Microvesseldensity analysis, the oncogene p53, immunophenotyping, hematoxylin/eosin(H/E) morphological analysis, undifferentiated tumor classification,antibody titering, prominence of nucleoli, HIV p24, human papiloma virus(HPV; for cervical biopsy), and mitotic index; generally any diagnosticmethod utilizing staining techniques is within the scope of the presentinvention. The invention has utility in the field of oncology for theearly detection of minimal residual disease (“micrometastases”).

For the HER2/neu application, the Automated Cellular Imaging System(ACIS™) analyzes at least two subsamples: the H/E prepared slide and theimmunohistochemistry prepared slide. A histological reconstruction ofsubsamples is displayed and used for the appropriate reviewing. Aninstrument automatically selects an area of interest, using color spacetransformations (and morphological filtering, if necessary) to identifyand quantify the expression of the HER2/neu in the area of tissueselected from the immunohistochemistry prepared slide. The quantitativeresult, as well as any selected areas of interest from either slide, isincorporated into the patient report.

The following acronyms and terminology are used throughout thisdocument: A worklist is a group of specimens that is prepared with thesame staining techniques and typically analyzed with the same automatedapplication. A specimen group consists of one or more patient cases. Ahistological reconstruction is an image of the whole specimen that hasbeen mounted on a slide. This image is created by piecing together morethan one fields of view at any objective. Unless otherwise defined, alltechnical and scientific terms used herein have the same meaning ascommonly understood by one of ordinary skill in the art to which thisinvention belongs.

Although methods and materials similar or equivalent to those describedherein can be used in the practice or testing of the present invention,suitable methods and materials are described below.

All publications, patent applications, patents, and other referencesmentioned herein are incorporated by reference in their entirety. Incase of conflict, the present application, including definitions, willcontrol. In addition, the materials and methods described herein areillustrative only and not intended to be limiting. Other features andadvantages of the invention will be apparent from the following detaileddescription, the drawings, and from the claims.

Preferred Workflow

A preferred workflow is provided in the following outline:

1. Specimens arrive in the laboratory. The specimens are labeled inaccordance with standard hospital procedures to insure the chain ofcustody.

2. Sample Preparation.

2.1 Two or three tissue sections per patient case are preferable forthis test.

2.2 Mount one piece of the tissue to a slide, to be stained with H/E.The H/E staining protocol is compatible with HER2/neuimmunohistochemistry staining.

2.3 Mount the second piece of the tissue to a slide, to be stained withthe anti-HER2immunohistochemistry staining system.

2.4 If deemed necessary, mount a third piece of the tissue to a thirdslide, to be stained with a third antibody (i.e., patient negativecontrol) and the anti-HER2 staining system.

2.5 To avoid ACIS™ mechanical limitations, the specimen placementspecifications is followed. Considering the frosted end of the slide asthe top, specimen must be below 30 mm and above 65 mm from the top edge.The specimen may reach to the far side edges of the slide.

2.6 Other patient samples? If yes, repeat section 2.

2.7 Include two stain control slides.

3. Sample Staining.

3.1 Stain all H/E prepared slides with the H/E staining protocol.

3.2 Stain all immunohistochemistry slides with anti-HER2 stainingsystem.

3.3 If deemed necessary, stain all negative immunohistochemistry slideswith a third antibody (i.e., patient negative control) and the anti-HER2staining system.

3.4 Stain the positive and negative control slides with the anti-HER2staining system and the appropriate antibodies.

4. Barcode all prepared slides and place within open slots in the slidecarriers.

5. Manually study the positive and negative control slides and verifythe staining is in control. Manually study the patient's positive andnegative immunohistochemistry slides and verify patient doesn't have anabnormality in background staining.

6. Work-list entry

6.1 Enter the information that is consistent per work-list.

6.1.1 Specify the work-list identification.

6.1.2 Specify the protocol/application (HER2/neu)

6.1.3 Specify the barcodes of the positive and negative control slides

6.2 Enter the information that is related to each patient case.

6.2.1 Accession number and Patient Identification (ID)

6.2.2 Specify the two/three barcodes of the samples for this case (H/E,immunohistochemistry, patient negative control)

6.2.3 Patient specific information: Age (integer), Sex (M/F),Race(character—predefined selections), Diagnosis (character—2S6), Date ofdiagnosis (Date Format), Date of last treatment (Date Format), andDescription of last treatment (character—512).

7. If daily calibration of the instrument hasn't been completed, pleasedo so before starting the work-list (5 min per calibration, necessaryonce per a day).

This calibration consists of radiometric, geometric, and color standardcalibration.

8. Load the carriers containing the control slides and patient casesinto the input hopper.

9. Start the automated analysis of the work-list.

9.1 Should the stain controls not have a large enough delta (Δ), theinstrument skips the rest of the slides within the worklist.

10. After completion of the analysis, review the results of thework-list and construct the appropriate reports online. For each case,the low power H/E image appears automatically with the selection of thepatient ID, accession number, or sample identification.

10.1 While viewing the low power H/E Histological Reconstruction (HR)the user has the following choices:

10.1.1 Select an area of the H/E HR to zoom on.

10.1.2 Choose to go to the low power immunohistochemistry HR.

10.1.3 Select an area to save within the pre-constructed report format.

10.2 While viewing the high power H/E HR the user has the followingchoices:

10.2.1 Choose to go back to the low power H/E HR.

10.3 While viewing the low power immunohistochemistry HR the user hasthe following choices:

10.3.1 Select an area of the immunohistochemistry HR to zoom on.

10.3.2 Select an area of the low power immunohistochemistry HR and queryfor the following quantitative results. The quantitative results arerelative to the scoring of the positive stain control and the patient'snegative immunohistochemistry slide.

10.3.2.1 Over-expression within selected area reported as 0, 1+, 2+, or3+.

10.3.2.2 Percentage of positive cells within the selected area.

10.4 While viewing the high power immunohistochemistry HR the user hasthe following choices:

10.4.1 Select an area of the high power immunohistochemistry HR andquery for the following quantitative results. The quantitative resultsare relative to the scoring of the positive stain control and thepatient's negative immunohistochemistry slide.

10.4.1.1 Over-expression within selected area reported as 0, 1+, 2+, or3+.

10.4.1.2 Percentage of positive cells within the selected area.

10.4.2 Choose to go back to the low power immunohistochemistry HR.

10.5 Other samples to review?

10.5.1 If yes, repeat section 8.

10.6 Should the analysis result in the tissue not being scanned, reloadslide, open application, and adjust scan area to location of the tissue,if necessary. Should the scan area be adequate, but the focus points notfall on an area of tissue, resulting in failed focus, in the applicationchange the focus inset to a number closer to one and confirm that thescan area is centered on an area of tissue. Execute the application.Repeat steps 10.1-10.5.

10.7 Manually record results of analysis.

11. Quality Assurance checks within the instrument.

11.1 At the completion of analyzing both the positive and negativecontrol slides, the Δ between the scores are calculated. If this Δ isn'tlarge enough, the analysis of all slides in this work-list is flaggedaccordingly and the instrument warns the user with an audible alarm.

11.2 If the between the patient's positive and negativeimmunohistochemistry slide isn't large enough, the patient is flaggedaccordingly.

11.3 A montage of tissue sections from both the positive and negativecontrols. Five tissue section images from each are adequate.

12. Preview the reports online and modify as necessary.

13. Send the reports to the printer or by the Internet to the requestingclinicians. The output of the worklist is a stained positive controlslide, a stained negative control slide, two stained slides per patientcase (one H/E and another IHC), and a comprehensive electronic report.The report includes the selected image sections, quantitative results,reference material, and other pertinent data.

Sample Staining

A cellular specimen (a “sample”) is split to provide two or moresubsamples. The term “sample” includes cellular material derived from asubject. Such samples include but are not limited to hair, skin samples,tissue sample, cultured cells, cultured cell media, and biologicalfluids. The term “tissue” refers to a mass of connected cells (e.g., CNStissue, neural tissue, or eye tissue) derived from a human or otheranimal and includes the connecting material and the liquid material inassociation with the cells. The term “biological fluid” refers to liquidmaterial derived from a human or other animal. Such biological fluidsinclude, but are not limited to, blood, plasma, serum, serumderivatives, bile, phlegm, saliva, sweat, amniotic fluid, andcerebrospinal fluid (CSF), such as lumbar or ventricular CSF. The term“sample” also includes media containing isolated cells. The quantity ofsample required to obtain a reaction may be determined by one skilled inthe art by standard laboratory techniques. The optimal quantity ofsample may be determined by serial dilution.

The HER2/neu method uses an anti-HER2/neu staining system, such as acommercially available kit, like that provided by DAKO (Carpinteria,Calif.). The steps for the immunohistochemistry protocol are as follows:(1) Prepare wash buffer solution. (2) Deparaffinize and rehydratespecimens. (3) Perform epitope retrieval. Incubate 40 min in a 95° C.water bath. Cool slides for 20 min at room temperature. (4) Applyperoxidase blocking reagent. Incubate 5 min. (5) Apply primary antibodyor negative control reagent. Incubate 30 min +/−1 min at roomtemperature. Rinse in wash solution. Place in wash solution bath. (6)Apply peroxidase labeled polymer. Incubate 30 min +/−1 min at roomtemperature. Rinse in wash solution. Place in wash solution bath. (7)Prepare DAB substrate chromagen solution. (8) Apply substrate chromogensolution (DAB). Incubate 5-10 min. Rinse with distilled water; (9)Counterstain. (10) Mount coverslips. The slide includes a cover-slipmedium to protect the sample and to introduce optical correctionconsistent with microscope objective requirements. The coverslip coversthe entire prepared specimen. Mounting the coverslip does not introduceair bubbles obscuring the stained specimen. This coverslip couldpotentially be a mounted 1½ thickness coverslip with DAKO Ultramountmedium. (11) A set of staining control slides are run with everyworklist. The set includes a positive and negative control. The positivecontrol is stained with the anti-HER2 antibody and the negative isstained with another antibody. Both slides are identified with a uniquebarcode. Upon reading the barcode, the instrument recognizes the slideas part of a control set, and runs the appropriate application. Theremay be one or two applications for the stain controls. (12) A set ofinstrument calibration slides includes the slides used for focus andcolor balance calibration. (13) A dedicated carrier is used forone-touch calibration. Upon successful completion of this calibrationprocedure, the instrument reports itself to be calibrated. Uponsuccessful completion of running the standard slides, the user is ableto determine whether the instrument is within standards and whether theinter-instrument and intra-instrument repeatability of test results.

The hematoxylin/eosin (H/E) slides are prepared with a standard H/Eprotocol. Standard solutions include the following: (1) Gillshematoxylin (hematoxylin 6.0 g; aluminium sulphate 4.2 g; citric acid1.4 g; sodium iodate 0.6 g; ethylene glycol 269 ml; distilled water 680ml); (2) eosin (eosin yellowish 1.0 g; distilled water 100 ml); (3)lithium carbonate 1% (lithium carbonate 1 g; distilled water 100 g); (4)acid alcohol 1% 70% (alcohol 99 ml conc.; hydrochloric acid 1 ml); and(5) Scott's tap water. In a beaker containing 1 L distilled water, add20 g sodium bicarbonate and 3.5 g magnesium sulphate. Add a magneticstirrer and mix thoroughly to dissolve the salts. Using a filter funnel,pour the solution into a labeled bottle.

The staining procedure is as follows: (1) Bring the sections to water;(2) place sections in hematoxylin for 5 min; (3) wash in tap water; (4)‘blue’ the sections in lithium carbonate or Scott's tap water; (5) washin tap water; (6) place sections in 1% acid alcohol for a few seconds;(7) wash in tap water; (8)place sections in eosin for 5 min; (9) wash intap water; and (10) dehydrate, clear. Mount sections.

The results of the H/E staining provide cells with nuclei stainedblue-black, cytoplasm stained varying shades of pink; muscle fibersstained deep pinky red; fibrin stained deep pink; and red blood cellsstained orange-red.

The invention also provides automated methods for analysis of estrogenreceptor and progesterone receptor. The estrogen and progesteronereceptors, like other steroid hormone receptors, plays a role indevelopmental processes and maintenance of hormone responsiveness intarget cells. From the molecular viewpoint, estrogen and progesteronereceptor interaction with target genes is of paramount importance inmaintenance of normal cell function and is also involved in regulationof mammary tumor cell function. The expression of progesterone receptorand estrogen receptor in breast tumors is a useful indicator forsubsequent hormone therapy. An anti-estrogen receptor antibody labelsepithelial cells of breast carcinomas which express estrogen receptor.An immunohistochemical assay of the estrogen receptor is performed usingan anti-estrogen receptor antibody, for example the well-characterized1D5 clone, and the methods of Pertchuk et al. (Cancer 77: 2514_(—)2519,1996) or a commercially available immunohistochemistry system such asthat provided by DAKO (Carpenteria CA; DAKO LSAB2 ImmunostainingSystem).

In breast carcinoma cells, immunohistochemistry immunostaining ofprogesterone receptor has been demonstrated in the nuclei of cells fromvarious histologic subtypes. An anti-progesterone receptor antibodylabels epithelial cells of breast carcinomas which express progesteronereceptor. An immunohistochemical assay of the progesterone receptor isperformed using an anti-estrogen receptor antibody, for example thewell-characterized 1A6 clone, and the methods of Pertchuk et al. (Cancer77: 2514-2519, 1996).

Still other automated analyses are within the scope of the invention,including the following:

Micrometastases/Metastatic Recurring Disease (MM/MRD).

Metastasis is the biological process whereby a cancer spreads to thedistant part of the body from its original site. A micrometastases isthe presence of a small number of tumor cells, particularly in the lymphnodes and bone marrow. A metastatic recurring disease is similar tomicrometastasis, but is detected after cancer therapy rather than beforetherapy. An immunohistochemical assay for MM/MRD is performed using amonoclonal antibody which reacts with an antigen (a metastatic-specificmucin) found in bladder, prostate and breast cancers.

MIB-1. MIB-1 is an antibody that can be used in immunohistochemicalassays for the antigen Ki-67. The clinical stage at first presentationis related to the proliferative index measured with Ki-67. High indexvalues of Ki-67 are positively correlated with metastasis, death fromneoplasia, low disease-free survival rates, and low overall survivalrates.

Microvessel Density Analysis. Microvessel density analysis is a measureof new blood vessel formation (angiogenesis). Angiogenesis ischaracteristic of growing tumors. Intratumor microvessel density can beassessed by anti-CD34 immunostaining.

The Oncogene p53. Overexpression of the p53 oncogene has been implicatedas the most common genetic alteration in the development of humanmalignancies. Investigations of a variety of malignancies, includingneoplasms of breast, colon, ovary, lung, liver, mesenchyme, bladder andmyeloid, have suggested a contributing role of p53 mutation in thedevelopment of malignancy. The highest frequency of expression has beendemonstrated in tumors of the breast, colon and ovary. A wide variety ofnormal cells do express a wildtype form of p53 but generally inrestricted amounts. Overexpression and mutation of p53 have not beenrecognized in benign tumors or in normal tissue. An immunohistochemicalassay of p53 is performed using an anti-p53 antibody, for example thewell-characterized DO-7 clone.

Immunophenotyping.

Undifferentiated tumor classification.

Antibody titering.

Prominence of Nucleoli. A nucleoli is an organelle in a cell nucleus.Cervical dysplasia refers to the replacement of the normal ormetaplastic epithelium with atypical epithelial cells that havecytologic features that are pre-malignant (nuclear hyperchromatism,nuclear enlargement and irregular outlines, increasednuclear-to-cytoplasmic ratio, increased prominence of nucleoli) andchromosomal abnormalities. The changes seen in dysplastic cells are ofthe same kind but of a lesser degree than those of frankly malignantcells. In addition, there are degrees of dysplasia (mild, moderate,severe).

HIV p24 Protein. Human immunodeficiency virus (HIV) p24 antigen levelsare measured in an immunohistochemistry assay using anti-HIV p24antigen. The HIV p24 protein test is used for the detection of HIVvirus. The test can be used to detect the virus, measure the amount ofvirus, and examine the virus' genetic composition.

Human Papiloma Virus (HPV). HPV is a sexually transmitted virus whichcauses warts. HPV is an important health concern because it may causecancer of the cervix. An immunohistochemical assay of HPV is performedusing an antibody associated with cervical cancer. Several serologicalresponses are strongly associated with cervical cancer, notably the IgGresponse against the HPV 16 epitopes LI: 13, E2:9, and E7:5, and the IgAresponse against an HPV 18 E2-derived antigen.

Mitotic Index. When a viral genome is incorporated into the nucleargenetic material, uncontrolled malignant growth of the host cell may bepromoted.

Operating the Instrument

While operating the instrument, the user interacts with a few screensincluding, but not limited to, Patient Entry, Review Data, ConstructReport, Manual Control, and User Preferences. Rather than theinformation entry revolving around the patient cases, the patient dataentry interface is organized as worklist information composed ofmultiple patient cases. For example, a worklist for HER2/neu is composedof tissue samples that are prepared in one of two methods, the HER2/neuimmunohistochemistry staining technique or the H/E technique. The slideswithin a HER2/neu worklist consists of a positive control slide(prepared with the immunohistochemistry technique and the HER2antibody), a negative control slide (prepared with theimmunohistochemistry technique and another antibody), and two slides forthe one or more patient cases. Per patient case there are two slidesprepared. One slide is prepared with the HER2/neu immunohistochemistrytechnique and a second is prepared with the H/E technique for generaltissue analysis.

The data that is relevant for a HER2/neu worklist is the: (1) worklistname or identifier of protocol (e.g., HER2/neu); barcode of the positivecontrol slide; and barcode of the negative control slide. The data thatis relevant on a per Patient Case is the: (4) patient ID; (5) accessionnumber; (6) social security number; (7) referring hospital, (8)referring physician; (9) barcode of the immunohistochemistry preparedslide; and (10) barcode of the H/E prepared slide.

The user starts analysis with the batch button and the slide carriersloaded into the input hopper. The input hopper is composed of all theimmunohistochemistry slides from the patient cases, all the H/E slidesfrom the patient cases, the positive control slide, and the negativecontrol slide. During analysis the instrument reads the barcode on theslide and determines one of three situations and starts one of the threeappropriate computer applications: (1) Slide is the positive controlslide and run the HER2-positive application. (2) Slide is the negativecontrol slide and run the HER2-negative application. (3) Slide is eitherthe immunohistochemistry or H/E patient slide and run the HER2application.

The system reads industry standard barcodes. The barcode relates thetype of protocol the instrument uses to analyze the slide. The type ofdata stored for each slide is determined by the stain preparation andthe protocol used to analyze it. During the transport of the slidethrough the system, therefore, the chain of custody is preserved. When abarcode is unreadable or isn't found in one of the defined worklists,the action taken by the instrument is to ignore the slide altogether andsend it to the output hopper.

The HER2-positive application (1) finds the specimen, (2) goes to fivelocations within the specimen, (3) stores the image from each location,(4) completes color space transformations to obtain the intensity ofstain at all five locations, and (5) stores the average value ofintensity in the database (DB). This value is used as the normalizedexpression value for the HER2 quantitative analysis. This value is alsoused to determine the between the positive and negative control slidesand verify that the is large enough.

The HER2-negative application (1) finds the specimen, (2) goes to fivelocations within the specimen, (3) stores the image from each location,(4) completes color space transformations to obtain the intensity ofstain at all five locations, and (5) stores the average value ofintensity in the database. This value is used to determine the Δ betweenthe positive and negative control slides and verify that the Δ is largeenough.

The HER2 application (1) finds the specimen, (2) scans the slide at 10×and constructs a histological reconstruction object, and (3) stores theobject in the database. Before a slide is analyzed, the instrumentchecks to see if the hard drive space has met a certain capacity forarchiving data. If so, the user is prompted to archive before continuinginstrument analysis. Compression may need to take place prior to theobject being stored within the database.

After completion of running all the slides within the worklist, thehistological reconstruction objects are analyzed by the pathologist orphysician.

The Slide Display Interface displays the list of slides that wereanalyzed, or the list of patient cases. Within tissue analysis, thereare always at least two slides per patient case that are analyzed by theinstrument. The set of patient slides is a logical unit. Therefore, apathologist or physician may choose the patient case by the patient IDor the accession number.

Within the User Interface that displays the list of patient cases to bereviewed/analyzed, once the pathologist chooses the patient case, H/Eimage is always displayed first. Using the H/E image, the pathologistdetermines whether or not the cancer is invasive and whether or notanalysis of the IHC slide is necessary.

Upon patient case selection, the low power H/E image is displayed. Thisimage can be reduced to enable the whole tissue cross-section to bedisplayed within the system's monitor (resolution at least 1024×760).

The pathologist can have the features available to choose more than onepatient case to review and then traverse from one case to the next withthe following features. For each patient case, the reduced low power H/Eimage always appears first.

While viewing the reduced low power H/E image the pathologist has thefollowing capabilities: (1) to zoom into an area of the low power H/Eimage for higher magnification; (2) to choose to go to the reduced lowpower immunohistochemistry image for the current patient case; (3) toselect an area to incorporate within the patient report (Marking animage section); (4) to choose to go to the next patient case (ifselected in previous user interface); and (5) to choose to go to theprevious patient case.

While viewing the zoomed H/E image the pathologist has the followingcapabilities: (1) to go back to the reduced low power H/E image; (2) tochoose to go to the reduced low power immunohistochemistry image for thecurrent patient case; (3) to select an area to incorporate within thepatient report (Marking an image section); (4) to choose to go to thenext patient case (if selected in previous user interface); and (5) tochoose to go to the previous patient case.

While viewing the reduced low power immunohistochemistry image thepathologist has the following capabilities: (1) to zoom into an area ofthe low power immunohistochemistry image for higher magnification; (2)to choose to go back to the reduced H/E image for the current patientcase; (3) to select an area of the reduced immunohistochemistry imageand query for quantitative results; (4) to select an area to incorporatewithin the patient report (Marking an image section); (5) to select aquantitative result to incorporate within the patient report (Save thequantitative result); (6) to choose to go to the next patient case (ifselected in previous user interface); and (7) to choose to go to theprevious patient case (if applicable).

While viewing the zoomed immunohistochemistry image the pathologist hasthe following capabilities: (1) to go back to the reduced low power IHCimage; (2) to choose to go back to the reduced H/E image for the currentpatient case; (3) to select an area of the zoomed immunohistochemistryimage and query for quantitative results; (4) to select an area toincorporate within the patient report (Marking an image section); (5) toselect a quantitative result to incorporate within the patient report(Save the quantitative result); (6) to choose to go to the next patientcase (if selected in previous user interface); and (7) to choose to goto the previous patient case (if applicable).

After completion of the pathologist analysis and review of all thepatient cases, the reports can be generated. A User Interface lists allthe patient cases that have already been reviewed and analyzed by thepathologist. The pathologist can choose one or more of the listedpatient cases to view the reports electronically. The HER2/neu reportfor a patient case can contain the following information: (1) PatientInformation and Demographics as entered within the Worklist InformationEntry; (2) Referring Physician and Hospital as entered within theWorklist Information Entry; (3) Pathologist and Laboratory Information;(4) Sections of the H/E image marked during the review/analysis forreport inclusion; (5) sections of the immunohistochemistry image markedduring the review/analysis for report inclusion; (5) quantitativeanalysis saved during the review/analysis for report inclusion; and (6)HER2/neu application and scoring analysis reference materials.

The interface that displays the patient reports online contains thefollowing functionality: (1) Print; (2) Send To, allows for (linked toe-mail, when available, and prompting the user for the address of whereto send the report); (3) Next Patient Case Report (if more than one casewas selected from the previous interface); (4) Previous Patient CaseReport (if applicable); and (5) Save/Apply (if the additional commentsarea is modified). Network connectivity is site specific. Sitespecificity is desired for sending reports via the Internet withouthaving to transport the report over to another host to send it. Directsending capabilities are preferred.

The instrument can have at least two user modes. One user mode is alocked protocol consistent with FDA regulations. A second user mode ismodifiable by the user. For the second user mode, access to thefollowing parameters of the application include: (1) definition of thescan area, center, width, and height; (2) toggle switch for whether ornot the Find phase is used; (3) Focus Type; (4) Focus Threshold; and (5)Focus Inset.

Automated System

Referring now to the figures, an apparatus for automated cell analysisof biological specimens is generally indicated by reference numeral 10as shown in perspective view in FIG. 1 and in block diagram form in FIG.2. The apparatus 10 comprises a microscope subsystem 32 housed in ahousing 12. The housing 12 includes a slide carrier input hopper 16 anda slide carrier output hopper 18. A door 14 in the housing 12 securesthe microscope subsystem from the external environment. A computersubsystem comprises a computer 22 having a system processor 23, an imageprocessor 25 and a communications modem 29. The computer subsystemfurther includes a computer monitor 26 and an image monitor 27 and otherexternal peripherals including storage device 21, pointing device 30,keyboard 28 and color printer 35. An external power supply 24 is alsoshown for powering the system. Viewing oculars 20 of the microscopesubsystem project from the housing 12 for operator viewing. Theapparatus 10 further includes a CCD camera 42 for acquiring imagesthrough the microscope subsystem 32. A microscope controller 31 underthe control of system processor 23 controls a number ofmicroscope-subsystem functions described further in detail. An automaticslide feed mechanism in conjunction with X-Y stage 38 provide automaticslide handling in the apparatus 10. An illumination light source 48projects light onto the X-Y stage 38 which is subsequently imagedthrough the microscope subsystem 32 and acquired through CCD camera 42for processing in the image processor 25. A Z stage or focus stage 46under control of the microscope controller 31 provides displacement ofthe microscope subsystem in the Z plane for focusing. The microscopesubsystem 32 further includes a motorized objective turret 44 forselection of objectives.

The purpose of the apparatus 10 is for the unattended automatic scanningof prepared microscope slides for the detection and counting ofcandidate objects of interest such as normal and abnormal cells, e.g.,tumor cells. The preferred embodiment may be utilized for rare eventdetection in which there may be only one candidate object of interestper several hundred thousand normal cells, e.g., one to five candidateobjects of interest per 2 square centimeter area of the slide. Theapparatus 10 automatically locates and counts candidate objects ofinterest and estimates normal cells present in a biological specimen onthe basis of color, size and shape characteristics. A number of stainsare used to preferentially stain candidate objects of interest andnormal cells different colors so that such cells can be distinguishedfrom each other.

As noted in the background of the invention, a biological specimen maybe prepared with a reagent to obtain a colored insoluble precipitate.The apparatus of the present invention is used to detect thisprecipitate as a candidate object of interest. During operation of theapparatus 10, a pathologist or laboratory technician mounts preparedslides onto slide carriers. A slide carrier 60 is illustrated in FIG. 8and will be described further below. Each slide carrier holds up to 4slides. Up to 25 slide carriers are then loaded into input hopper 16.The operator can specify the size, shape and location of the area to bescanned or alternatively, the system can automatically locate this area.The operator then commands the system to begin automated scanning of theslides through a graphical user 25 interface. Unattended scanning beginswith the automatic loading of the first carrier and slide onto theprecision motorized X-Y stage 38. A bar code label affixed to the slideis read by a bar code reader 33 during this loading operation. Eachslide is then scanned at a user selected low microscope magnification,for example, 10×, to identify candidate objects of interest based ontheir color, size, and shape characteristics. The X-Y locations ofcandidate cells are stored until scanning is completed.

After the low magnification scanning is completed, the apparatusautomatically returns to each candidate object of interest, reimages andrefocuses at a higher magnification such as 40× and performs furtheranalysis to confirm the object candidate. The apparatus stores an imageof the object for later review by a pathologist. All results and imagescan be stored to a storage device 21 such as a removable hard drive orDAT tape or transmitted to a remote site for review or storage. Thestored images for each slide can be viewed in a mosaic of images forfurther review. In addition, the pathologist or operator can alsodirectly view a detected object through the microscope using theincluded oculars 20 or on image monitor 27.

Having described the overall operation of the apparatus 10 from a highlevel, the further details of the apparatus will now be described.Referring to FIG. 3, the microscope controller 31 is shown in moredetail. The microscope controller 31 includes a number of subsystemsconnected through a system bus. A system processor 102 controls thesesubsystems and is controlled by the apparatus system processor 23through an RS 232 controller 110. The system processor 102 controls aset of motor—control subsystems 114 through 124 which control the inputand output feeder, the motorized turret 44, the X-Y stage 38, and the Zstage 46 (FIG. 2). A histogram processor 108 receives input from CCDcamera 42 for computing variance data during the focusing operationdescribed further herein. The system processor 102 further controls anillumination controller 106 for control of substage illumination 48. Thelight output from the halogen light bulb which supplies illumination forthe system can vary over time due to bulb aging, changes in opticalalignment, and other factors. In addition, slides which have been “overstained” can reduce the camera exposure to an unacceptable level. Inorder to compensate for these effects, the illumination controller 106is included. This controller is used in conjunction with light controlsoftware to compensate for the variations in light level. The lightcontrol software samples the output from the camera at intervals (suchas between loading of slide carriers), and commands the controller toadjust the light level to the desired levels. In this way, light controlis automatic and transparent to the user and adds no substantialadditional time to system operation. The system processor 23 may be anyprocessor capable of performing the operations of the system, forexample dual parallel Intel Pentium 90 MHZ devices, a T.I. C80 processorfor computation and processors capable of 8 to 32 bit operation on an NTsystem. The image processor 25 is preferably a Matrox Imaging Series 640model. The microscope controller system processor 102 is an AdvancedMicro Devices AMD29K device.

FIGS. 4 and 5 show further detail of the apparatus 10 is shown. FIG. 4shows a plan view of the apparatus 10 with the housing 12 removed. Aportion of the automatic slide feed mechanism 37 is shown to the left ofthe microscope subsystem 32 and includes slide carrier unloadingassembly 34 and unloading platform 36 which in conjunction with slidecarrier output hopper 18 function to receive slide carriers which havebeen analyzed. Vibration isolation mounts 40, shown in further detail inFIG. 5, are provided to isolate the microscope subsystem 32 frommechanical shock and vibration that can occur in a typical laboratoryenvironment. In addition to external sources of vibration, thehigh-speed operation of the X-Y stage 38 can induce vibration into themicroscope subsystem 32. Such sources of vibration can be isolated fromthe electro-optical subsystems to avoid any undesirable effects on imagequality. The isolation mounts 40 comprise a spring 40 a and piston 40 bsubmerged in a high viscosity silicon gel which is enclosed in anelastomer membrane bonded to a casing to achieve damping factors on theorder of 17 to 20%.

The automatic slide-handling feature of the present invention will nowbe described. The automated slide handling subsystem operates on asingle slide carrier at a time. A slide carrier 60 is shown in FIGS. 6aand 6 b which provide a top view and a bottom view respectively. Theslide carrier 60 includes up to four slides 70 mounted with adhesivetape 62. The carrier 60 includes ears 64 for hanging the carrier in theoutput hopper 18. An undercut 66 and pitch rack 68 are formed at the topedge of the slide carrier 60 for mechanical handling of the slidecarrier. A keyway cutout 65 is formed in one side of the carrier 60 tofacilitate carrier alignment. A prepared slide 72 mounted on the slidecarrier 60 includes a sample area 72 a and a bar code label area 72 b.FIG. 7a provides a top view of the slide handling subsystem whichcomprises a slide input module 15, a slide output module 17 and X-Ystage drive belt 50. FIG. 7b provides a partial cross-sectional viewtaken along line A—A of FIG. 7a. The slide input module 15 comprises aslide carrier input hopper 16, loading platform 52 and slide carrierloading subassembly 54. The input hopper 16 receives a series of slidecarriers 60 (FIGS. 6a and 6 b) in a stack on loading platform 52. Aguide key 57 protrudes from a side of the input hopper 16 to which thekeyway cutout 65 (FIG. 6a) of the carrier is fit to achieve proper 15alignment. The input module 15 further includes a revolving indexing cam56 and a switch 90 mounted in the loading platform 52, the operation ofwhich is described further below. The carrier loading subassembly 54comprises an infeed drive belt 59 driven by a motor 86. The infeed drivebelt 59 includes a pusher tab 58 for pushing the slide carrierhorizontally toward the 20 X-Y stage 38 when the belt is driven. Ahoming switch 95 senses the pusher tab 58 during a revolution of thebelt 59.

Referring specifically to FIG. 7a, the X-Y stage 38 is shown with xposition and y position motors 96 and 97 respectively which arecontrolled by the microscope controller 31 (FIG. 3) and are notconsidered part of the slide handling subsystem. The X-Y stage 38further includes an aperture 55 for allowing illumination to reach theslide carrier. A switch 91 is mounted adjacent the aperture 55 forsensing contact with the carrier and thereupon activating a motor 87 todrive stage drive belt 50 (FIG. 7b). The drive belt 50 is a double-sidedtiming belt having teeth for engaging pitch rack 68 of the carrier 60(FIG. 6b).

The slide output module 17 includes slide carrier output hopper 18,unloading platform 36 and slide carrier unloading subassembly 34. Theunloading subassembly 34 comprises a motor 89 for rotating the unloadingplatform 36 about shaft 98 during an unloading operation describedfurther below. An outfeed gear 93 driven by motor 88 rotatably engagesthe pitch rack 68 of the carrier 60 (FIG. 6b) to transport the carrierto a rest position against switch 92. A springloaded hold-down mechanismholds the carrier in place on the unloading platform 36.

The slide handling operation will now be described. Referring to FIG. 8,a series of slide carriers 60 are shown stacked in input hopper 16 withthe top edges 60 a aligned. As the slide handling operation begins, theindexing cam 56 driven by motor 85 advances one revolution to allow onlyone slide carrier to drop to the bottom of the hopper 16 and onto theloading platform 52.

FIGS. 8a-8 d show the cam action in more detail. The cam 56 includes ahub 56 a to which are mounted upper and lower leaves 56 b and 56 crespectively. The leaves 56 b, 56 c are semicircular projectionsoppositely positioned and spaced apart vertically. In a first positionshown in FIG. 8a, the upper leaf 56 b supports the bottom carrier at theundercut portion 66. At a 20 position of the cam 56 rotated 180°, shownin FIG. 8b, the upper leaf 56 b no longer supports the carrier andinstead the carrier has dropped slightly and is supported by the lowerleaf 56 c. FIG. 8c shows the position of the cam 56 rotated 270° whereinthe upper leaf 56 b has rotated sufficiently to begin to engage theundercut 66 of the next slide carrier while the opposite facing lowerleaf 56 c still supports the bottom carrier. After a full rotation of360° as shown in FIG. 8d, the lower leaf 56 c has rotated opposite thecarrier stack and no longer supports the bottom carrier which now restson the loading platform 52. At the same position, the upper leaf 56 bsupports the next carrier for repeating the cycle.

Referring again to FIGS. 7a and 7 b, when the carrier drops to theloading platform 52, the contact closes switch 90 which activates motors86 and 87. Motor 86 drives the infeed drive belt 59 until the pusher tab58 makes contact with the carrier and pushes the carrier onto the X-Ystage drive belt 50. The stage drive belt 50 advances the carrier untilcontact is made with switch 91, the closing of which begins the slidescanning process described further herein. Upon completion of thescanning process, the X-Y stage 38 moves to an unload position andmotors 87 and 88 are activated to transport the carrier to the unloadingplatform 36 using stage drive belt 50. As noted, motor 88 drives outfeedgear 93 to engage the carrier pitch rack 68 of the carrier 60 (FIG. 6b)until switch 92 is contacted. Closing switch 92 activates motor 89 torotate the unloading platform 36.

The unloading operation is shown in more detail in end views of theoutput module 17 (FIGS. 9a-9 d). In FIG. 9a, the unloading platform 36is shown in a horizontal position supporting a slide carrier 60. Thehold-down mechanism 94 secures the carrier 60 at one end. FIG. 9b showsthe output module 17 after motor 89 has rotated the unloading platform36 to a vertical position, at which point the spring loaded hold-downmechanism 94 releases the slide carrier 60 into the output hopper 18.The carrier 60 is supported in the output hopper 18 by means of ears 64(FIGS. 6a and 6 b). FIG. 9c shows the unloading platform 36 beingrotated back towards the 20 horizontal position. As the platform 36rotates upward, it contacts the deposited carrier 60 and the upwardmovement pushes the carrier toward the front of the output hopper 18.FIG. 9d shows the unloading platform 36 at its original horizontalposition after having output a series of slide carriers 60 to the outputhopper 18.

Having described the overall system and the automated slide handlingfeature, the aspects of the apparatus 10 relating to scanning, focusingand image processing will now be described in further detail.

In some cases, an operator will know ahead of time where the scan areaof interest is on the slide. Conventional preparation of slides forexamination provides repeatable and known placement of the sample on theslide. The operator can therefore instruct the system to always scan thesame area at the same location of every slide which is prepared in thisfashion. But there are other times in which the area of interest is notknown, for example, where slides are prepared manually with a knownsmear technique. One feature of the invention automatically determinesthe scan area using a texture analysis process. FIG. 10 is a flowdiagram that describes the processing associated with the automaticlocation of a scan area. As shown in this figure, the basic method is topre-scan the entire slide area to determine texture features thatindicate the presence of a smear and to discriminate these areas fromdirt and other artifacts.

At each location of this raster scan, an image such as in FIG. 12 isacquired and analyzed for texture information at steps 204 and 206.Since it is desired to locate the edges of the smear sample within agiven image, texture analyses are conducted over areas called windows78, which are smaller than the entire image as shown in FIG. 12. Theprocess iterates the scan across the slide at steps 208,210,212 and 214.

In the interest of speed, the texture analysis process is performed at alower magnification, preferably at a 4× objective. One reason to operateat low magnification is to image the largest slide area at any one time.Since cells do not yet need to be resolved at this stage of the overallimage analysis, the 4× magnification is preferred. On a typical slide,as shown in FIG. 11, a portion 72 b of the end of the slide 72 isreserved for labeling with identification information. Excepting thislabel area, the entire slide is scanned in a raster scan fashion 76 toyield a number of adjacent images. Texture values for each windowinclude the pixel variance over a window, the difference between thelargest and smallest pixel value within a window, and other indicators.The presence of a smear raises the texture values compared with a blankarea.

One problem with a smear from the standpoint of determining its locationis its non-uniform thickness and texture. For example, the smear islikely to be relatively thin at the edges and thicker towards the middledue to the nature of the smearing process. To accommodate for thenon-uniformity, texture analysis provides a texture value for eachanalyzed area. The texture value tends to gradually rise as the scanproceeds across a smear from a thin area to a thick area, reaches apeak, and then falls off again to a lower value as a thin area at theedge is reached. The problem is then to decide from the series oftexture values the beginning and ending, or the edges, of the smear. Thetexture values are fit to a square wave waveform since the texture datadoes not have sharp beginnings and endings.

After conducting this scanning and texture evaluation operation, onemust determine which areas of elevated texture values represent thedesired smear 74, and which represent undesired artifacts. This isaccomplished by fitting a step function, on a line-by-line basis to thetexture values in step 216. This function, which resembles a singlesquare wave across the smear with a beginning at one edge, and end atthe other edge, and an amplitude provides the means for discrimination.The amplitude of the best-fit step function is utilized to determinewhether smear or dirt is present since relatively high values indicatesmear. If it is decided that smear is present, the beginning and endingcoordinates of this pattern are noted until all lines have beenprocessed, and the smear sample area defined at 218.

After an initial focusing operation described further herein, the scanarea of interest is scanned to acquire images for image analysis. Thepreferred method of operation is to initially perform a complete scan ofthe slide at low magnification to identify and locate candidate objectsof interest, followed by further image analysis of the candidate objectsof interest at high magnification in order to confirm the objects ascells. An alternate method of operation is to perform high magnificationimage analysis of each candidate object of interest immediately afterthe object has been identified at low magnification. The lowmagnification scanning then resumes, searching for additional candidateobjects of interest. Since it takes on the order of a few seconds tochange objectives, this alternate method of operation would take longerto complete.

The operator can pre-select a magnification level to be used for thescanning operation. A low magnification using a 10× objective ispreferred for the scanning operation since a larger area can beinitially analyzed for each acquired scan image. The overall detectionprocess for a cell includes a combination of decisions made at both low(10×) and high magnification (40×) levels. Decision making at the 10×magnification level is broader in scope, i.e., objects that loosely fitthe relevant color, size and shape characteristics are identified at the10× level.

Analysis at the 40× magnification level then proceeds to refine thedecision-making and confirm objects as likely cells or candidate objectsof interest. For example, at the 40× level it is not uncommon to findthat some objects that were identified at 10× are artifacts which theanalysis process will then reject. In addition, closely packed objectsof interest appearing at 10× are separated at the 40× level.

In a situation where a cell straddles or overlaps adjacent image fields,image analysis of the individual adjacent image fields could result inthe cell being rejected or undetected. To avoid missing such cells, thescanning operation compensates by overlapping adjacent image fields inboth the x and y directions. An overlap amount greater than half thediameter of an average cell is preferred. In the preferred embodiment,the overlap is specified as a percentage of the image field in the x andy directions.

The time to complete an image analysis can vary depending upon the sizeof the scan area and the number of candidate cells, or objects ofinterest identified. For one example, in the preferred embodiment, acomplete image analysis of a scan area of two square centimeters inwhich 50 objects of interest are confirmed can be performed in about 12to 15 minutes. This example includes not only focusing, scanning, andimage analysis, but also the saving of 40× images as a mosaic on harddrive 21 (FIG. 2). Consider the utility of the present invention in a“rare event” application where there may be one, two or a very smallnumber of cells of interest located somewhere on the slide. Toillustrate the nature of the problem by analogy, if one were to scale aslide to the size of a football field, a tumor cell, for example, wouldbe about the size of a bottle cap. The problem is then to rapidly searchthe football field and find the very small number of bottle caps andhave a high certainty that none have been missed.

However the scan area is defined, an initial focusing operation must beperformed on each slide prior to scanning. This is required since slidesdiffer, in general, in their placement in a carrier. These differencesinclude slight (but significant) variations of tilt of the slide in itscarrier. Since each slide must remain in focus during scanning, thedegree of tilt of each slide must be determined. This is accomplishedwith an initial focusing operation that determines the exact degree oftilt, so that focus can be maintained automatically during scanning.

The initial focusing operation and other focusing operations to bedescribed later utilize a focusing method based on processing of imagesacquired by the system. This method was chosen for its simplicity overother methods including use of IR beams reflected from the slide surfaceand use of mechanical gauges. These other methods also would notfunction properly when the specimen is protected with a coverglass. Thepreferred method results in lower system cost and improved reliabilitysince no additional parts need be included to perform focusing. FIG. 13Aprovides a flow diagram describing the “focus point” procedure. Thebasic method relies on the fact that the pixel value variance (orstandard deviation) taken about the pixel value mean is maximum at bestfocus. A “brute-force” method could simply step through focus, using thecomputer controlled Z or focus stage, calculate the pixel variance ateach step, and return to the focus position providing the maximumvariance. Such a method would be too time consuming. Therefore,additional features were added as shown in FIG. 13A.

These features include the determination of pixel variance at arelatively coarse number of focal positions, and then the fitting of acurve to the data to provide a faster means of determining optimalfocus. This basic process is applied in two steps, coarse and fine.During the coarse step at 220-230, the Z stage is stepped over auser-specified range of focus positions, with step sizes that are alsouser-specified. It has been found that for coarse focusing, these dataare a close fit to a Gaussian function. Therefore, this initial set ofvariance versus focus position data are least-squares fit to a Gaussianfunction at 228. The location of the peak of this Gaussian curvedetermines the initial or coarse estimate of focus position for input tostep 232.

Following this, a second stepping operation 232-242 is performedutilizing smaller steps over a smaller focus range centered on thecoarse focus position. Experience indicates that data taken over thissmaller range are generally best fit by a second order polynomial. Oncethis least squares fit is performed at 240, the peak of the second ordercurve provides the fine focus position at 244.

FIG. 14 illustrates a procedure for how this focusing method is utilizedto determine the orientation of a slide in its carrier. As shown, focuspositions are determined, as described above, for a 3×3 grid of pointscentered on the scan area at 264. Should one or more of these points lieoutside the scan area, the method senses at 266 this by virtue of lowvalues of pixel variance. In this case, additional points are selectedcloser to the center of the scan area. FIG. 15 shows the initial arrayof points 80 and new point 82 selected closer to the center. Once thisarray of focus positions is determined at 268, a least squares plane isfit to this data at 270. Focus points lying too far above or below thisbest-fit plane are discarded at 272 (such as can occur from a dirtycover glass over the scan area), and the data is then refit. This planeat 274 then provides the desired Z position information for maintainingfocus during scanning.

After determination of the best-fit focus plane, the scan area isscanned in an X raster scan over the scan area as described earlier.During scanning, the X stage is positioned to the starting point of thescan area, the focus (Z) stage is positioned to the best fit focusplane, an image is acquired and processed as described herein, and thisprocess is repeated for all points over the scan area. In this way,focus is maintained automatically without the need for time-consumingrefocusing at points during scanning. Prior to confirmation of cellobjects at a 40× or 60× level, a refocusing operation is conducted sincethe use of this higher magnification requires more precise focus thanthe best-fit plane provides. FIG. 16 provides the flow diagram for thisprocess. As may be seen, this process is similar to the fine focusmethod described earlier in that the object is to maximize the imagepixel variance. This is accomplished by stepping through a range offocus positions with the Z stage at 276, 278, calculating the imagevariance at each position at 278, fitting a second order polynomial tothese data at 282, and calculating the peak of this curve to yield anestimate of the best focus position at 284, 286. This final focusingstep differs from previous ones in that the focus range and focus stepsizes are smaller since this magnification requires focus settings towithin 0.5 micron or better. It should be noted that for somecombinations of cell staining characteristics, improved focus can beobtained by numerically selecting the focus position that provides thelargest variance, as opposed to selecting the peak of the polynomial. Insuch cases, the polynomial is used to provide an estimate of best focus,and a final step selects the actual Z position giving highest pixelvariance. It should also be noted that if at any time during thefocusing process at 40× or 60× the parameters indicate that the focusposition is inadequate, the system automatically reverts to a coarsefocusing process as described above with reference to FIG. 13A. Thisensures that variations in specimen thickness can be accommodated in anexpeditious manner. For some biological specimens and stains, thefocusing methods discussed above do not provide optimal focused results.For example, certain white blood cells known as neutrophils may bestained with Fast Red, a commonly known stain, to identify alkalinephosphatase in the cytoplasm of the cells. To further identify thesecells and the material within them, the specimen may be counterstainedwith hemotoxylin to identify the nucleus of the cells. In cells sotreated, the cytoplasm bearing alkaline phosphatase becomes a shade ofred proportionate to the amount of alkaline phosphatase in the cytoplasmand the nucleus becomes blue. However, where the cytoplasm and nucleusoverlap, the cell appears purple. These color combinations appear topreclude the finding of a focused Z position using the focus processesdiscussed above.

In an effort to find a best focal position at high magnification, afocus method, such as the one shown in FIG. 13B, may be used. Thatmethod begins by selecting a pixel near the center of a candidate objectof interest (Block 248) and defining a region of interest centered aboutthe selected pixel (Block 250). Preferably, the width of the region ofinterest is a number of columns which is a power of 2. This widthpreference arises from subsequent processing of the region of interestpreferably using a one dimensional Fast Fourier Transform (FFT)technique. As is well known within, the art, processing columns of pixelvalues using the FFT technique is facilitated by making the number ofcolumns to be processed a power of two. While the height of the regionof interest is also a power of two in the preferred embodiment, it neednot be unless a two dimensional FFT technique is used to process theregion of interest.

After the region of interest is selected, the columns of pixel valuesare processed using the preferred one-dimensional FFT to determine aspectra of frequency components for the region of interest (Block 252).The frequency spectra ranges from DC to some highest frequencycomponent. For each frequency component, a complex magnitude iscomputed. Preferably, the complex magnitudes for the frequencycomponents which range from approximately 25% of the highest componentto approximately 75% of the highest component are squared and summed todetermine the total power for the region of interest (Block 254).Alternatively, the region of interest may be processed with a smoothingwindow, such as a Hanning window, to reduce the spurious high frequencycomponents generated by the FFT processing of the pixel values in theregion of interest. Such preprocessing of the region of interest permitsall complex magnitude over the complete frequency range to be squaredand summed. After the power for a region has been computed and stored(Block 256), a new focal position is selected, focus adjusted (Blocks258, 260), and the process repeated. After each focal position has beenevaluated, the one having the greatest power factor is selected as theone best in focus (Block 262).

The following describes the image processing methods which are utilizedto decide whether a candidate object of interest such as a stained tumorcell is present in a given image, or field, during the scanning process.Candidate objects of interest which are detected during scanning arereimaged at higher (40× or 60×) magnification, the decision confirmed,and a region of interest for this cell saved for later review by thepathologist. The image processing includes color space conversion, lowpass filtering, background suppression, artifact suppression,morphological processing, and blob analysis. One or more of these stepscan optionally be eliminated. The operator is provided with an option toconfigure the system to perform any or all of these steps and whether toperform certain steps more than once or several times in a row. Itshould also be noted that the sequence of steps may be varied andthereby optimized for specific reagents or reagent combinations;however, the sequence described herein is preferred. It should be notedthat the image processing steps of low pass filtering, thresholding,morphological processing, and blob analysis are generally known imageprocessing building blocks.

An overview of the preferred process is shown in FIG. 17A. The preferredprocess for identifying and locating candidate objects of interest in astained biological specimen on a slide begins with an acquisition ofimages obtained by scanning the slide at low magnification (Block 288).Each image is then converted from a first color space to a second colorspace (Block 290) and the color converted image is low pass filtered(Block 292). The pixels of the low pass filtered image are then comparedto a threshold (Block 294) and, preferably, those pixels having a valueequal to or greater than the threshold are identified as candidateobject of interest pixels and those less than the threshold aredetermined to be artifact or background pixels. The candidate object ofinterest pixels are then morphologically processed to identify groups ofcandidate object of interest pixels as candidate objects of interest(Block 296). These candidate objects of interest are then compared toblob analysis parameters (Block 298) to further differentiate candidateobjects of interest from objects which do not conform to the blobanalysis parameters and, thus, do not warrant further processing. Thelocation of the candidate objects of interest may be stored prior toconfirmation at high magnification. The process continues by determiningwhether the candidate objects of interest have been confirmed (Block300). If they have not been confirmed, the optical system is set to highmagnification (Block 302) and images of the slide at the locationscorresponding to the candidate objects of interest identified in the lowmagnification images are acquired (Block 288). These images are thencolor converted (Block 290), low pass filtered (Block 292), compared toa threshold (Block 294), morphologically processed (Block 296), andcompared to blob analysis parameters (Block 298) to confirm whichcandidate objects of interest located from the low magnification imagesare objects of interest. The coordinates of the objects of interest arethen stored for future reference (Block 303).

In general, the candidate objects of interest, such as tumor cells, aredetected based on a combination of characteristics, including size,shape, and color. The chain of decision-making based on thesecharacteristics preferably begins with a color space conversion process.The CCD camera coupled to the microscope subsystem outputs a color imagecomprising a matrix of 640×480 pixels. Each pixel comprises red, green,and blue (ROB) signal values.

It is desirable to transform the matrix of RGB values to a differentcolor space because the difference between candidate objects of interestand their background, such as tumor and normal cells, may be determinedfrom their respective colors. Specimens are generally stained with oneor more industry standard stains (e.g., DAB, New Fuchsin, AEC) which are“reddish” in color. Candidate objects of interest retain more of thestain and thus appear red while normal cells remain unstained. Thespecimens may also be counterstained with hematoxalin so the nuclei ofnormal cells or cells not containing an object of interest appear blue.In addition to these objects, dirt and debris can appear as black, gray,or can also be lightly stained red or blue depending on the stainingprocedures utilized. The residual plasma or other fluids also present ona smear may also possess some color.

In the color conversion operation, a ratio of two of the RGB signalvalues is formed to provide a means for discriminating colorinformation. With three signal values for each pixel, nine differentratios can be formed: RlR, R/G, RUB, G/G, G/B, G/R, B/B, B/G, B/R. Theoptimal ratio to select depends upon the range of color informationexpected in the slide specimen. As noted above, typical stains used fordetecting candidate objects of interest such as tumor cells arepredominantly red, as opposed to predominantly green or blue. Thus, thepixels of a cell of interest which has been stained contain a redcomponent which is larger than either the green or blue components. Aratio of red divided by blue (R/B) provides a value which is greaterthan one for tumor cells but is approximately one for any clear or whiteareas on the slide. Since the remaining cells, i.e., normal cells,typically are stained blue, the R/B ratio for pixels of these lattercells yields values of less than one. The R/B ratio is preferred forclearly separating the color information typical in these applications.

FIG. 17B illustrates the flow diagram by which this conversion isperformed. In the interest of processing speed, the conversion isimplemented with a look up table. The use of a look up table for colorconversion accomplishes three functions: (1) performing a divisionoperation; (2) scaling the result for processing as an image havingpixel values ranging from 0 to 255; and (3) defining objects which havelow pixel values in each color band (R,G,B) as “black” to avoid infiniteratios (i.e., dividing by zero). These “black” objects are typicallystaining artifacts or can be edges of bubbles caused by pasting acoverglass over the specimen.

Once the look up table is built at 304 for the specific color ratio(i.e., choices of tumor and nucleated cell stains), each pixel in theoriginal RGB image is converted at 308 to produce the output. Since itis of interest to separate the red stained tumor cells from blue stainednormal ones, the ratio of color values is then scaled by a userspecified factor. As an example, for a factor of 128 and the ratio of(red pixel value)/(blue pixel value), clear areas on the slide wouldhave a ratio of 1 scaled by 128 for a final X value of 128. Pixels whichlie in red stained tumor cells would have X value greater than 128,while blue stained nuclei of normal cells would have value less than128. In this way, the desired objects of interest can be numericallydiscriminated. The resulting 640×480 pixel matrix, referred to as theX-image, is a gray scale image having values ranging from 0 to 255.

Other methods exist for discriminating color information. One classicalmethod converts the RGB color information into another color space, suchas HSI (hue, saturation, intensity) space. In such a space, distinctlydifferent hues such as red, blue, green, yellow, may be readilyseparated. In addition, relatively lightly stained objects may bedistinguished from more intensely stained ones by virtue of differingsaturations. However, converting from RGB space to HSI space requiresmore complex computation. Conversion to a color ratio is faster; forexample, a full image can be converted by the ratio technique of thepresent invention in about 30 ms while an HSI conversion can takeseveral seconds.

In yet another approach, one could obtain color information by taking asingle color channel from the camera. As an example, consider a bluechannel, in which objects that are red are relatively dark. Objectswhich are blue, or white, are relatively light in the blue channel. Inprinciple, one could take a single color channel, and simply set athreshold wherein everything darker than some threshold is categorizedas a candidate object of interest, for example, a tumor cell, because itis red and hence dark in the channel being reviewed. However, oneproblem with the single channel approach occurs where illumination isnot uniform. Non-uniformity of illumination results in non-uniformityacross the pixel values in any color channel, for example, tending topeak in the middle of the image and dropping off at the edges where theillumination falls off. Performing thresholding on this non-uniformcolor information runs into problems, as the edges sometimes fall belowthe threshold, and therefore it becomes more difficult to pick theappropriate threshold level. However, with the ratio technique, if thevalues of the red channel fall off from center to edge, then the valuesof the blue channel also fall off center to edge, resulting in a uniformratio. non-uniformities. Thus, the ratio technique is more immune toillumination.

As previously described, the color conversion scheme is relativelyinsensitive to changes in color balance, i.e., the relative outputs ofthe red, green, and blue channels. However, some control is necessary toavoid camera saturation, or inadequate exposures in any one of the colorbands. This color balancing is performed automatically by utilizing acalibration slide consisting of a clear area, and a “dark” area having aknown optical transmission or density. The system obtains images fromthe clear and “dark” areas, calculates “white” and “black” adjustmentsfor the image processor 25, and thereby provides correct color balance.

In addition to the color balance control, certain mechanical alignmentsare automated in this process. The center point in the field of view forthe various microscope objectives as measured on the slide can vary byseveral (or several tens of) microns. This is the result of slightvariations in position of the microscope objectives 44 a as determinedby the turret 44 (FIG. 4), small variations in alignment of theobjectives with respect to the system optical axis, and other factors.Since it is desired that each microscope objective be centered at thesame point, these mechanical offsets must be measured and automaticallycompensated.

This is accomplished by imaging a test slide which contains arecognizable feature or mark. An image of this pattern is obtained bythe system with a given objective, and the position of the markdetermined. The system then rotates the turret to the next lensobjective, obtains an image of the test object, and its position isredetermined. Apparent changes in position of the test mark are recordedfor this objective. This process is continued for all objectives. Oncethese spatial offsets have been determined, they are automaticallycompensated for by moving the stage 38 by an equal (but opposite) amountof offset during changes in objective. In this way, as different lensobjectives are selected, there is no apparent shift in center point orarea viewed. A low pass filtering process precedes thresholding. Anobjective of thresholding is to obtain a pixel image matrix having onlycandidate objects of interest, such as tumor cells above a thresholdlevel and everything else below it. However, an actual acquired imagewill contain noise. The noise can take several forms, including whitenoise and artifacts. The microscope slide can have small fragments ofdebris that pick up color in the staining process and these are known asartifacts. These artifacts are generally small and scattered areas, onthe order of a few pixels, which are above the threshold. The purpose oflow pass filtering is to essentially blur or smear the entire colorconverted image. The low pass filtering process will smear artifactsmore than larger objects of interest. Such as tumor cells and therebyeliminate or reduce the number of artifacts that pass the thresholdingprocess. The result is a cleaner thresholded image downstream. In thelow pass filter process, a 3×3 matrix of coefficients is applied to eachpixel in the 640×480 x-image. A preferred coefficient matrix is asfollows:

1/9 1/9 1/9

1/9 1/9 1/9

1/9 1/9 1/9

At each pixel location, a 3×3 matrix comprising the pixel of interestand its neighbors is multiplied by the coefficient matrix and summed toyield a single value for the pixel of interest. The output of thisspatial convolution process is again a 640×480 matrix. As an example,consider a case where the center pixel and only the center pixel, has avalue of 255 and each of its other neighbors, top left, top, top rightand so forth, have values of 0.

This singular white pixel case corresponds to a small object. The resultof the matrix multiplication and addition using the coefficient matrixis a value of 1/9 (255) or 28 for the center pixel, a value which isbelow the nominal threshold of 128. Now consider another case in whichall the pixels have a value of 255 corresponding to a large object.Performing the low pass filtering operation on a 3×3 matrix for thiscase yields a value of 255 for the center pixel. Thus, large objectsretain their values while small objects are reduced in amplitude oreliminated. In the preferred method of operation, the low pass filteringprocess is performed on the X image twice in succession.

In order to separate objects of interest, such as a tumor cell in the ximage from other objects and background, a thresholding operation isperformed designed to set pixels within cells of interest to a value of255, and all other areas to 0. Thresholding ideally yields an image inwhich cells of interest are white and the remainder of the image isblack. A problem one faces in thresholding is where to set the thresholdlevel. One cannot simply assume that cells of interest are indicated byany pixel value above the nominal threshold of 128. A typical imagingsystem may use an incandescent halogen light bulb as a light source. Asthe bulb ages, the relative amounts of red and blue output can change.The tendency as the bulb ages is for the blue to drop off more than thered and the green. To accommodate for this light source variation overtime, a dynamic thresholding process is used whereby the threshold isadjusted dynamically for each acquired image. Thus, for each 640×480image, a single threshold value is derived specific to that image.

As shown in FIG. 18, the basic method is to calculate, for each field,the mean X value, and the standard deviation about this mean at 312. Thethreshold is then set at 314 to the mean plus an amount defined by theproduct of a (user specified) factor and the standard deviation of thecolor converted pixel values. The standard deviation correlates to thestructure and number of objects in the image. Preferably, the userspecified factor is in the range of approximately 1.5 to 2.5. The factoris selected to be in the lower end of the range for slides in which thestain has primarily remained within cell boundaries and the factor isselected to be in the upper end of the range for slides in which thestain is pervasively present throughout the slide. In this way, as areasare encountered on the slide with greater or lower backgroundintensities, the threshold may be raised or lowered to help reducebackground objects. With this method, the threshold changes in step withthe aging of the light source such that the effects of the aging arecanceled out. The image matrix resulting at 316 from the thresholdingstep is a binary image of black (O) 5 and white (255) pixels.

As is often the case with thresholding operations such as that describedabove, some undesired areas will lie above the threshold value due tonoise, small stained cell fragments, and other artifacts. It is desiredand possible to eliminate these artifacts by virtue of their small sizecompared with legitimate cells of interest. Morphological processes areutilized to perform this function.

Morphological processing is similar to the low pass filter convolutionprocess described earlier except that it is applied to a binary image.Similar to spatial convolution, the morphological process traverses aninput image matrix, pixel by pixel, and places the processed pixels inan output matrix. Rather than calculating a weighted sum of neighboringpixels as m the low pass convolution process, the morphological processuses set theory operations to combine neighboring pixels in a nonlinearfashion.

Erosion is a process whereby a single pixel layer is taken away from theedge of an object. Dilation is the opposite process which adds a singlepixel layer to the edges of an object. The power of morphologicalprocessing is that it provides for further discrimination to eliminatesmall objects that have survived the thresholding process and yet arenot likely tumor cells. The erosion and dilation processes that make upa morphological “open” preferably make small objects disappear yetallows large objects to remain. Morphological processing of binaryimages is described in detail in “Digital Image Processing”, pages127-137, G.A. Baxes, John Wiley & Sons (1994).

FIG. 19 illustrates the flow diagram for this process. As shown here, amorphological “open” process performs this suppression. A singlemorphological open consists of a single morphological erosion 320followed by a single morphological dilation 322. Multiple “opens”consist of multiple erosions followed by multiple dilations. In thepreferred embodiment, one or two morphological opens are found to besuitable. At this point in the processing chain, the processed imagecontains thresholded objects of interest, such as tumor cells (if anywere present in the original image), and possibly some residualartifacts that were too large to be eliminated by the processes above.

FIG. 20 provides a flow diagram illustrating a blob analysis performedto determine the number, size, and location of objects in thethresholded image. A blob is defined as a region of connected pixelshaving the same “color,” in this case, a value of 255. Processing isperformed over the entire image to determine the number of such regionsat 324 and to determine the area and x,y coordinates for each detectedblob at 326. Comparison of the size of each blob to a known minimum areaat 328 for a tumor cell allows a refinement in decisions about whichobjects are objects of interest, such as tumor cells, and which areartifacts. The location (x,y coordinates) of objects identified as cellsof interest in this stage are saved for the final 40× reimaging stepdescribed below. Objects not passing the size test are disregarded asartifacts.

The processing chain described above identifies objects at the scanningmagnification as cells of interest candidates. As illustrated in FIG.21, at the completion of scanning, the system switches to the 40×magnification objective at 330, and each candidate is reimaged toconfirm the identification 332. Each 40× image is reprocessed at 334using the same steps as described above but with test parameterssuitably modified for the higher magnification (e.g., area). At 336, aregion of interest centered on each confirmed cell is saved to the harddrive for review by the pathologist.

As noted earlier, a mosaic of saved images is made available for viewingby the pathologist. As shown in FIG. 22, a series of images of cellswhich have been confirmed by the image analysis is presented in themosaic 150. The pathologist can then visually inspect the images to makea determination whether to accept (152) or reject (153) each cell image.Such a 5 determination can be noted and saved with the mosaic of imagesfor generating a printed report.

In addition to saving the image of the cell and its region, the cellcoordinates are saved should the pathologist wish to directly view thecell through the oculars or on the image monitor. In this case, thepathologist reloads the slide carrier, selects the slide and cell forreview from a mosaic of cell images, and the system automaticallypositions the cell under the microscope for viewing.

It has been found that normal cells whose nuclei have been stained withhematoxylin are often quite numerous, numbering in the thousands per 10×image. Since these cells are so numerous, and since they tend to clump,counting each individual nucleated cell would add an excessiveprocessing burden, at the expense of speed, and would not necessarilyprovide an accurate count due to clumping. The apparatus performs anestimation process in which the total area of each field that is stainedhematoxylin blue is measured and this area is divided by the averagesize of a nucleated cell. FIG. 23 outlines this process. In thisprocess, a single color band (the red channel provides the best contrastfor blue stained nucleated cells) is processed by calculating theaverage pixel value for each field at 342, establishing two thresholdvalues (high and low) as indicated at 344, 346, and counting the numberof pixels between these two values at 348. In the absence of dirt, orother opaque debris, this provides a count of the number ofpredominantly blue pixels. By dividing this value by the average areafor a nucleated cell at 350, and looping over all fields at 352, anapproximate cell count is obtained. Preliminary testing of this processindicates an accuracy with +/−15%. It should be noted that for someslide preparation techniques, the size of nucleated cells can besignificantly larger than the typical size. The operator can select theappropriate nucleated cell size to compensate for these characteristics.

As with any imaging system, there is some loss of modulation transfer(i.e., contrast) due to the modulation transfer function (MTF)characteristics of the imaging optics, camera, electronics, and othercomponents. Since it is desired to save “high quality” images of cellsof interest both for pathologist review and for archival purposes, it isdesired to compensate for these MTF losses. An MTF compensation, orMTFC, is performed as a digital process applied to the acquired digitalimages. A digital filter is utilized to restore the high spatialfrequency content of the images upon storage, while maintaining lownoise levels. With this MTFC technology, image quality is enhanced, orrestored, through the use of digital processing methods as opposed toconventional oil-immersion or other hardware based methods. MTFC isdescribed further in “The Image Processing Handbook,” pages 225 and 337,J. C. Rues, CRC Press (1995).

Referring to FIG. 24, the functions available in a user interface of theapparatus 10 are shown. From the user interface, which is presentedgraphically on computer monitor 26, an operator can select amongapparatus functions which include acquisition 402, analysts 404, andsystem configuration 406. At the acquisition level 402, the operator canselect between manual 408 and automatic 410 modes of operation. In themanual mode, the operator is presented with manual operations 409.Patient information 414 regarding an assay can be entered at 412. In theanalysis level 404, review 416 and report 418 functions are madeavailable. At the review level 416, the operator can select a montagefunction 420. At this montage level, a pathologist can performdiagnostic review functions including visiting an image 422,accept/reject of cells 424, nucleated cell counting 426, accept/rejectof cell counts 428, and saving of pages at 430. The report level 418allows an operator to generate patient reports 432. In the configurationlevel 406, the operator can select to configure preferences at 434,input operator information 437 at 436, create a system log at 438, andtoggle a menu panel at 440. The configuration preferences include scanarea selection functions at 442, 452; montage specifications at 444, barcode handling at 446, default cell counting at 448, stain selection at450, and scan objective selection at 454.

Computer Implementation

Aspects of the invention may be implemented in hardware or software, ora combination of both. However, preferably, the algorithms and processesof the invention are implemented in one or more computer programsexecuting on programmable computers each comprising at least oneprocessor, at least one data storage system (including volatile andnon-volatile memory and/or storage elements), at least one input device,and at least one output device. Program code is applied to input data toperform the functions described herein and generate output information.The output information is applied to one or more output devices, inknown fashion.

Each program may be implemented in any desired computer language(including machine, assembly, high level procedural, or object orientedprogramming languages) to communicate with a computer system. In anycase, the language may be a compiled or interpreted language.

Each such computer program is preferably stored on a storage media ordevice (e.g., ROM, CD-ROM, tape, or magnetic diskette) readable by ageneral or special purpose programmable computer, for configuring andoperating the computer when the storage media or device is read by thecomputer to perform the procedures described herein. The inventivesystem may also be considered to be implemented as a computer-readablestorage medium, configured with a computer program, where the storagemedium so configured causes a computer to operate in a specific andpredefined manner to perform the functions described herein.

A number of embodiments of the present invention have been described.Nevertheless, various modifications may be made without departing fromthe spirit and scope of the invention. Accordingly, the invention is notto be limited by the specific illustrated embodiment, but only by thescope of the appended claims.

What is claimed:
 1. A method for automated detection of a cellproliferative disorder, comprising, a) providing a sample on a slide,wherein the sample is contacted with an antibody that binds to a proteinassociated with a cell proliferative disorder; b) identifying aprocessing parameter for the sample of a); c) scanning the sample of b)at a plurality of locations at a low magnification on an optical system;d) acquiring a low magnification image at the low magnification at eachlocation in the scanned area; e) transforming substantially all pixelsof the image from a first color space to a second color space; f)processing each low magnification image by means of a computer processorto detect rare candidate objects of interest in said second color spacewith reference to a predetermined threshold; g) storing stagecoordinates of each location for each candidate object of interest; h)adjusting the optical system to a higher magnification; i) repositioningthe stage to the location for each candidate object of interest; j)acquiring a higher magnification image of each candidate object ofinterest; and k) storing each higher magnification image.
 2. The methodof claim 1, wherein the cell proliferative disorder is a neoplasm. 3.The method of claim 1, wherein the cell proliferative disorder is breastcancer.
 4. The method of claim 1, wherein the antibody is selected fromthe group consisting of an anti-HER2/neu antibody, anti-estrogenreceptor antibody, anti-progesterone receptor antibody, anti-p53antibody and anti-cyclin D1 antibody.
 5. The method of claim 1, whereinthe processing parameter is selected from the group consisting of apositive control, negative control or test sample.
 6. The method ofclaim 1, wherein the detection of the candidate object of interest is bysize, color or shape.